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剪切波弹性成像和应变成像技术对乳腺病变的诊断价值。

Diagnostic values of shear wave elastography and strain elastography for breast lesions.

机构信息

Department of Ultrasonography, Weihai Central Hospital, Weihai, China.

Blood Purification Center, Weihai Central Hospital, Weihai, China.

出版信息

Rev Med Chil. 2020 Sep;148(9):1239-1245. doi: 10.4067/S0034-98872020000901239.

Abstract

BACKGROUND

Strain elastography (SE) and shear wave elastography (SWE) have high diagnostic yield for breast lesions, but the optimal parameters remain elusive.

AIM

To evaluate the diagnostic yield of SWE and SE for breast lesions by multivariate logistic regression analysis.

MATERIAL AND METHODS

A total of 132 patients with 164 breast tumors were enrolled. Breast lesions were classified with the breast imaging reporting and data system (BI-RADS). Maximum (Emax), mean (Emean) and standard deviation (Esd) of elastic modulus, lesion/fat elasticity ratio and elastographic classification were obtained by SWE. Strain ratio (SR) and elastographic score were obtained by SE. A multivariate logistic regression analysis was performed. The diagnostic efficiencies of BI-RADS classification, SWE, SE and their combination were compared plotting ROC curves.

RESULTS

There were 110 benign and 54 malignant lesions which had significantly different SWE and SE parameters. The parameters included in the logistic regression were Esd and elastographic classification obtained by SWE and the elastographic score obtained by SE. When combining SWE with SE, Esd, SR and SWE classification were included in the equation. The areas under ROC curves for BI-RADS classification, SWE, SE and their combination were 0.75, 0.88, 0.79 and 0.89, respectively.

CONCLUSIONS

The diagnostic value of SWE in combination with SE for breast lesions exceeded that of SE or SWE alone. Esd showed a good diagnostic yield when SWE was used alone or combined with SE.

摘要

背景

应变弹性成像(SE)和剪切波弹性成像(SWE)对乳腺病变具有较高的诊断率,但最佳参数仍不明确。

目的

通过多变量逻辑回归分析评估 SWE 和 SE 对乳腺病变的诊断效能。

材料与方法

共纳入 132 例 164 个乳腺病灶的患者。乳腺病变采用乳腺影像报告和数据系统(BI-RADS)分类。通过 SWE 获得弹性模量的最大值(Emax)、平均值(Emean)和标准差(Esd)、病变/脂肪弹性比值和弹性图像分类。通过 SE 获得应变比(SR)和弹性评分。进行多变量逻辑回归分析。通过绘制 ROC 曲线比较 BI-RADS 分类、SWE、SE 及其联合应用的诊断效率。

结果

110 个良性病灶和 54 个恶性病灶的 SWE 和 SE 参数存在显著差异。纳入逻辑回归的参数包括 SWE 获得的 Esd 和弹性图像分类以及 SE 获得的弹性评分。当 SWE 与 SE 联合应用时,纳入方程的参数包括 Esd、SR 和 SWE 分类。BI-RADS 分类、SWE、SE 及其联合应用的 ROC 曲线下面积分别为 0.75、0.88、0.79 和 0.89。

结论

SWE 联合 SE 对乳腺病变的诊断价值优于 SE 或 SWE 单独应用。当单独使用 SWE 或与 SE 联合应用时,Esd 显示出良好的诊断效能。

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